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International Conference on Autonomous Agents and Multiagent Systems

作者： Tathagata Chakraborti Yu Zhang David E. Smith Subbarao Kambhampati Department of Computer Science Arizona State University Autonomous Systems & Robotics Intelligent Systems Division NASA Ames Research Center

ISBN: (纸本)9781510855083

In order to be acceptable members of future human-robot ecosystems, it is necessary for autonomous agents to be respectful of the intentions of humans cohabiting a workspace and account for conflicts on shared resources in the environment. In this paper we build an integrated system that demonstrates how maintaining predictive models of its human colleagues can inform the planning process of the robotic agent. We propose an Integer Programming based planner as a general formulation of this flavor of "human-aware" planning and show how the proposed formulation can be used to produce different behaviors of the robotic agent, showcasing compromise, opportunism or negotiation. Finally, we investigate how the proposed approach scales with the different parameters involved, and provide empirical evaluations to illustrate the pros and cons associated with the proposed style of planning.

关键词： Cohabitation Conflict (Psychology) Planning Intelligent agents Integer Programming Planning process Workplace Flavoring Agents Human

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Image processing applications through a variational perceptually-based color correction related to Retinex

Image processing applications through a variational perceptu...

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Retinex at 50

作者： Vazquez-Corral, Javier Zamir, Syed Waqas Galdran, Adrian Pardo, David Bertalmío, Marcelo Information and Communication Technologies Department Universitat Pompeu Fabra Barcelona Spain Tecnalia Computer Vision Group Derio Spain Department of Applied Mathematics Statistics and Operational Research University of the Basque Country Leioa Spain BCAM Basque Center for Applied Mathematics Bilbao Spain IKERBASQUE Basque Foundation for Science Bilbao Spain

The retinex theory of color vision has been a light of inspiration for color researchers since its inception 50 years ago. It has been adapted to work for different goals such as shadow removal, high dynamic range imaging, or computational color constancy. Back in 2007 a variational perceptually-based color correction model related to Retinex was presented by Bertalmío and colleagues. In this paper we first comment on this model and later we review different image processing applications that have been obtained by performing small modifications to it, namely color gamut mapping and image dehazing. © 2016 Society for Imaging science and Technology.

关键词： Color

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CELL PROPOSAL NETWORK FOR MICROSCOPY IMAGE ANALYSIS

CELL PROPOSAL NETWORK FOR MICROSCOPY IMAGE ANALYSIS

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IEEE International Conference on Image Processing

作者： Saad Ullah Akram Juho Kannala Lauri Eklund Janne Heikkila Center for Machine Vision Research University of Oulu Finland Department of Computer Science Aalto University Finland Biocenter Oulu University of Oulu Finland

ISBN: (纸本)9781467399623

Robust cell detection plays a key role in the development of reliable methods for automated analysis of microscopy images. It is a challenging problem due to low contrast, variable fluorescence, weak boundaries, conjoined and overlapping cells, causing most cell detection methods to fail in difficult situations. One approach for overcoming these challenges is to use cell proposals, which enable the use of more advanced features from ambiguous regions and/or information from adjacent frames to make better decisions. However, most current methods rely on simple proposal generation and scoring methods, which limits the performance they can reach. In this paper, we propose a convolutional neural network based method which generates cell proposals to facilitate cell detection, segmentation and tracking. We compare our method against commonly used proposal generation and scoring methods and show that our method generates significantly better proposals, and achieves higher final recall and average precision.

关键词： Proposals Shape Feature extraction Image segmentation Microscopy Training Image analysis

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Studying bioluminescence flashes with the ANTARES deep Sea neutrino telescope

arXiv

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arXiv 2021年

作者： Reeb, Nico Hutschenreuter, S. Zehetner, P. Ensslin, T. Albert, A. Alves, S. André, M. Anghinolfi, M. Anton, G. Ardid, M. Aubert, J.-J. Aublin, J. Baret, B. Basa, S. Belhorma, B. Bendahman, M. Bertin, V. Biagi, S. Bissinger, M. Boumaaza, J. Bouta, M. Bouwhuis, M.C. Brânzaş, H. Bruijn, R. Brunner, J. Busto, J. Caiffi, B. Capone, A. Caramete, L. Carr, J. Carretero, V. Celli, S. Chabab, M. Chau, T.N. Chiarusi, T. Circella, M. Coleiro, A. Colomer-Molla, M. Coniglione, R. Coyle, P. Creusot, A. Díaz, A.F. de Wasseige, G. Deschamps, A. Distefano, C. Di Palma, I. Domi, A. Donzaud, C. Dornic, D. Drouhin, D. van Eeden, T. Enzenhöfer, A. Fermani, P. Ferrara, G. Filippini, F. Fusco, L. Gatelet, Y. Gay, P. Glotin, H. Gozzini, R. Ruiz, R. Gracia Graf, K. Guidi, C. Hallmann, S. van Haren, H. Heijboer, A.J. Hello, Y. Hernández-Rey, J.J. Hößl, J. Hofestädt, J. Huang, F. Illuminati, G. James, C.W. Jisse-Jung, B. de Jong, M. de Jong, P. Jongen, M. Kadler, M. Kalekin, O. Katz, U. Khan-Chowdhury, N.R. Kouchner, A. Kreykenbohm, I. Kulikovskiy, V. Lahmann, R. Le Breton, R. Lefèvre, D. Leonora, E. Levi, G. Lincetto, M. Lopez-Coto, D. Loucatos, S. Maderer, L. Manczak, J. Marcelin, M. Margiotta, A. Marinelli, A. Martínez-Mora, J.A. Melis, K. Migliozzi, P. Moussa, A. Muller, R. Nauta, L. Navas, S. Nezri, E. Fearraigh, B.Ó. Organokov, M. Pavalaş, G.E. Pellegrino, C. Perrin-Terrin, M. Piattelli, P. Pieterse, C. Poirè, C. Popa, V. Pradier, T. Randazzo, N. Reck, S. Riccobene, G. Romanov, A. Sánchez-Losa, A. Greus, F. Salesa Samtleben, D.F.E. Sanguineti, M. Sapienza, P. Schnabel, J. Schumann, J. Schüssler, F. Spurio, M. Stolarczyk, Th. Taiuti, M. Tingay, S.J. Vallage, B. van Elewyck, V. Versari, F. Viola, S. Vivolo, D. Wilms, J. Zavatarelli, S. Zegarelli, A. Zornoza, J.D. Zúñiga, J. Information Field Theory Group Max Planck Institute for Astrophysics Garching Germany Informatics 6 Chair of Robotics Artificial Intelligence and Real time Systems Technical University of Munich Garching Germany Department of Astrophysics IMAPP Radboud University Nijmegen Nijmegen Netherlands Department of Physics Ludwig-Maximilians University Munich Germany CERN Geneva Switzerland Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace MulhouseF-68200 France IFIC - Instituto de Física Corpuscular CSIC Universitat de València c/ Catedrático José Beltrán 2 Paterna ValenciaE-46980 Spain Technical University of Catalonia Laboratory of Applied Bioacoustics Rambla Exposició Vilanova i la Geltrú Barcelona08800 Spain INFN - Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Centre for Astroparticle Physics Erwin-Rommel-Str. 1 Erlangen91058 Germany Universitat Politècnica de València C/ Paranimf 1 Gandia46730 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université de Paris CNRS Astroparticule et Cosmologie ParisF-75013 France Aix Marseille Univ CNRS CNES LAM Marseille France National Center for Energy Sciences and Nuclear Techniques B.P.1382 RabatR. P. 10001 Morocco University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 R.P. Rabat10000 Morocco Via S. Sofia 62 Catania95123 Italy University Mohammed I Laboratory of Physics of Matter and Radiations B.P.717 Oujda6000 Morocco Nikhef Science Park Amsterdam Netherlands Institute of Space Science Bucharest MagureleRO-077125 Romania Universiteit van Amsterdam Instituut voor Hoge-Energie Fysica Science Park 105 Amsterdam1098 XG Netherlands INFN - Sezione di Roma P.le Aldo Moro 2 Roma00185 Italy Dipartimento di Fisica Università La Sapienza P.le Aldo Moro 2 Roma00185 Italy LPHEA Faculty of Science - Semlali Cadi Ayyad University P.O

We develop a novel technique to exploit the extensive data sets provided by underwater neutrino telescopes to gain information on bioluminescence in the deep sea. The passive nature of the telescopes gives us the unique opportunity to infer information on bioluminescent organisms without actively interfering with them. We propose a statistical method that allows us to reconstruct the light emission of individual organisms, as well as their location and movement. A mathematical model is built to describe the measurement process of underwater neutrino telescopes and the signal generation of the biological organisms. The Metric Gaussian Variational Inference algorithm is used to reconstruct the model parameters using photon counts recorded by the neutrino detectors. We apply this method to synthetic data sets and data collected by the ANTARES neutrino telescope. The telescope is located 40 km off the French coast and fixed to the sea floor at a depth of 2475 m. The runs with synthetic data reveal that we can reliably model the emitted bioluminescent flashes of the organisms. Furthermore, we find that the spatial resolution of the localization of light sources highly depends on the configuration of the telescope. Precise measurements of the efficiencies of the detectors and the attenuation length of the water are crucial to reconstruct the light emission. Finally, the application to ANTARES data reveals the first precise localizations of bioluminescent organisms using neutrino telescope data. © 2021, CC BY.

关键词： Bioluminescence

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Nonverbal signaling for non-humanoid robots during human-robot collaboration 16

Nonverbal signaling for non-humanoid robots during human-rob...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Elizabeth Cha Maja Matarić Terrence Fong Department of Computer Science University of Southern California Los Angeles CA Intelligent Robotics Group NASA Ames Research Center Mountain View CA

ISBN: (纸本)9781467383707

Non-humanoid robots are becoming increasingly utilized for collaborative tasks across many domains, including industrial and service settings. Collaborative tasks between the human and robot rely on each collaborator's ability to effectively convey their mental state while accurately estimating and interpreting their partner's knowledge, intent, and actions. My research focuses on nonverbal communication signals that a non-humanoid robot can utilize during human-robot collaboration. We focus on motion, light and sound as they are commonly used communication channels across many domains and are available on most robot platforms. As a first step towards this goal, I present a completed study exploring how to use a simple multimodal light and sound signal to request help during a collaborative task. We then discuss future work to generate and utilize more complex signals to convey a variety of statuses to improve collaboration.

关键词： Robot kinematics Collaboration Service robots Robot sensing systems Autonomous automobiles Vehicles

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Joint cell segmentation and tracking using cell proposals

Joint cell segmentation and tracking using cell proposals

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IEEE International Symposium on Biomedical Imaging

作者： Saad Ullah Akram Juho Kannala Lauri Eklund Janne Heikkilä Biocenter Oulu Department of Computer Science Aalto University Finland Faculty of Biochemistry and Molecular Medicine University of Oulu Finland Center for Machine Vision Research

ISBN: (纸本)9781479923519

Time-lapse microscopy imaging has advanced rapidly in last few decades and is producing large volume of data in cell and developmental biology. This has increased the importance of automated analyses, which depend heavily on cell segmentation and tracking as these are the initial stages when computing most biologically important cell properties. In this paper, we propose a novel joint cell segmentation and tracking method for fluorescence microscopy sequences, which generates a large set of cell proposals, creates a graph representing different cell events and then iteratively finds the most probable path within this graph providing cell segmentations and tracks. We evaluate our method on three datasets from ISBI Cell Tracking Challenge and show that our greedy nonoptimal joint solution results in improved performance compared with state of the art methods.

关键词： Proposals Image segmentation Image edge detection Tracking Object segmentation Microscopy

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A generalized structure from motion framework for central projection cameras

Communications in Computer and Information Science

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Communications in computer and Information science 2016年 598卷 256-273页

作者： Gava, Christiano Couto Stricker, Didier Department Augmented Vision German Research Center for Artificial Intelligence Trippstadterstr. 122 Kaiserslautern67663 Germany Department of Computer Science Kaiserslautern University of Technology Gottlieb-Daimler str Kaiserslautern67663 Germany

This paper presents a novel Structure from Motion (SfM) framework designed for central projection cameras. The goal is to support future large scale multi-view 3D reconstruction algorithms. We believe that these algorithms will be able to benefit from several different sources of visual information. Accordingly, SfM approaches will need to handle this variety of image sources, such as perspective, wide-angle and spherical images. However, this issue has not yet been addressed. Current state of the art techniques are not able to handle heterogeneous images simultaneously. Therefore, we introduce SPHERA, a generalized SfM framework designed for central projection cameras. By adopting the unit sphere as underlying model it is possible to treat single effective viewpoint cameras in a unified way. We validate our framework on synthetic and real datasets. Results show that SPHERA is a powerful framework to support upcoming algorithms and applications on large scale 3D reconstruction. © Springer International Publishing Switzerland 2016.

关键词： Cameras

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A 3D simulation for social human-robot interaction with ontology-based robot knowledge

A 3D simulation for social human-robot interaction with onto...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Euijun Hwang Sangyun Lee Yoonseob Lim Jongsuk Choi Department of Computer Science and Information System University of Auckland Auckland New Zealand Department of Electrical Engineering Seoul National University of Science and Technology Seoul Korea Center for Robotics Research Korea Institute of Science and Technology Seoul Korea

This paper introduces a preliminary result for social human-robot interaction architecture and its application. System includes human perception module that receives visual data from 3D human simulation and knowledge module that contains various concepts for social interaction situation. The proposed system has been tested for inferring social interaction type based on a simple social interaction scenario.

关键词： Robots

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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User-adaptive fall detection for patients using wristband

User-adaptive fall detection for patients using wristband

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Young-Hoon Nho Jong Gwan Lim Dae-Eon Kim Dong-Soo Kwon Robotics Program and Human-Robot Interaction research center Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Information Engineering Computer Science and University of Trento Italy KAIST Human-Robot Interaction Research Center Daejeon Republic of Korea Department of Mechanical Engineering Human-Robot Interaction research center Daejeon Republic of Korea

Fall detection systems have been proposed to prevent additional injuries following fall accidents. This paper introduces an easily learnable fall detection system based on the data of an individual patient in a hospital room. The improvement of low performance using a single accelerometer at wrists and the inconvenience of sensor attached to a waist in the conventional approach was concentrated on by integrating heart rate signals to the conventional acceleration approach and changing the sensor location from a waist to wrists. As for the optimal heart rate feature selection, we proposed a four-feature vector combination (root mean square of successive differences, standard deviation of successive differences, normal to normal 50, normal to normal 20) with correlation and mutual information analysis in addition to mean absolute deviation selected as an accelerometer feature. To easily acquire and train the patients' fall data, our system was based on unsupervised learning approaches using Gaussian mixture models for optimal classifiers with the optimal cluster number decided by cluster validation index of square error sum. A 10-fold cross validation was applied for a final performance evaluation where each threshold for separating fall state from non-fall state was automatically decided in several comparison groups, which were created on the basis of fusion timing and used sensors. As a result, despite sensors attached to the wrist, the wearable inconvenience of the conventional is overcome using the feature-level fused approach between heart rates and accelerations with the accuracy up to 98.39 %, which is closest to 99.34 % of the case using a single accelerometer located at the waist.

关键词： Accelerometers Heart rate Wrist Training data Hospitals Robot sensing systems Unsupervised learning

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